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---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-large
This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5042
- Accuracy: 0.4286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 220
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6619 | 0.03 | 7 | 2.7017 | 0.0 |
| 2.6232 | 1.03 | 14 | 2.6628 | 0.0 |
| 2.381 | 2.03 | 21 | 2.5798 | 0.1667 |
| 2.2215 | 3.03 | 28 | 2.4757 | 0.1667 |
| 1.7389 | 4.03 | 35 | 2.3636 | 0.2333 |
| 1.3366 | 5.03 | 42 | 2.2424 | 0.3 |
| 1.1946 | 6.03 | 49 | 2.1675 | 0.3 |
| 0.6809 | 7.03 | 56 | 2.0548 | 0.3667 |
| 0.5255 | 8.03 | 63 | 2.0410 | 0.4 |
| 0.3285 | 9.03 | 70 | 1.9539 | 0.4 |
| 0.2849 | 10.03 | 77 | 1.8536 | 0.4667 |
| 0.1832 | 11.03 | 84 | 1.8293 | 0.4333 |
| 0.1307 | 12.03 | 91 | 1.8200 | 0.4 |
| 0.0901 | 13.03 | 98 | 1.8355 | 0.4 |
| 0.0636 | 14.03 | 105 | 1.8201 | 0.4333 |
| 0.0413 | 15.03 | 112 | 1.7750 | 0.4667 |
| 0.0427 | 16.03 | 119 | 1.7460 | 0.5333 |
| 0.0254 | 17.03 | 126 | 1.7804 | 0.5333 |
| 0.0203 | 18.03 | 133 | 1.8869 | 0.4333 |
| 0.0174 | 19.03 | 140 | 1.7741 | 0.5667 |
| 0.0154 | 20.03 | 147 | 1.7401 | 0.5333 |
| 0.0136 | 21.03 | 154 | 1.7672 | 0.5 |
| 0.0116 | 22.03 | 161 | 1.7793 | 0.5333 |
| 0.0123 | 23.03 | 168 | 1.8018 | 0.4667 |
| 0.0102 | 24.03 | 175 | 1.8024 | 0.5 |
| 0.0103 | 25.03 | 182 | 1.8058 | 0.5 |
| 0.0089 | 26.03 | 189 | 1.8106 | 0.5 |
| 0.0088 | 27.03 | 196 | 1.8029 | 0.5 |
| 0.0092 | 28.03 | 203 | 1.7961 | 0.5 |
| 0.0083 | 29.03 | 210 | 1.7940 | 0.5 |
| 0.0099 | 30.03 | 217 | 1.7922 | 0.5 |
| 0.0085 | 31.01 | 220 | 1.7920 | 0.5 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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